AI AND ETHICS and CONTEMPORARY PHILOSOPHY
Contemporary Philosophy - Role
Course Leader and Lecturer.
This module is an opportunity for you to learn about crucial developments in philosophy since the turn of the twentieth century and to improve your logic and intellectual ability to understand and successfully reason about any topic. The central topic of the course is meaning: what does it mean for something to mean something? (Confused? …exactly!)
We will be exploring three philosophical traditions in particular: analytic philosophy, ordinary language philosophy and phenomenology, while also examining the very exciting practical domain of Artificial Intelligence.
The first term focuses on the philosophy of language, while the second term focuses on the tradition of existential phenomenology and the philosophy of Artificial Intelligence.
The aim throughout will be for you to develop an understanding of contemporary philosophical methods, which should allow you to make headway in your thinking about a range of often baffling but always intriguing problems.
- Historical background to problems of contemporary philosophy. Descartes and Kant.
- Analytic philosophy and philosophy of language. The problem of meaning.
- Sense and nonsense. Peirce, the verification principle.
- The semantics/pragmatics interface. Meanings vs uses.
- The Mind/Body problem. Ryle's critique, Knowing-how and knowing-that.
- Speech act theory. Austin, ordinary language philosophy.
- Language-games and forms of life. Wittgenstein's critique of philosophy and meaning as use.
- Determinism and free will. Compatibilism, incompatibilism, neuroscience.
- Contemporary philosophy of the mind. The principle of alternate possibilities.
- Logic and reasoning. Using logic in philosophy and elsewhere.
- Continental philosophy. Phenomenology.
- Phenomenology of consciousness. Husserl's phenomenological reduction.
- The ontological difference. Heidegger, The principle that what is ontically closest is ontologically furthest away, Dasein, readiness-to-hand and readiness-at-hand.
- The analytic of Dasein. Heidegger's hermeneutic circle, 'authentic' being, his positive account of anxiety and existential analysis of death.
- Modern existentialism. Sartre's ontology, being-in-itself, being-for-itself, being-for-other, and authenticity. His existential analysis of love.
- Applications of existential phenomenology. De Beauvoir's application to feminist politics.
- Ethics and Artificial Intelligence. Ethics, AI ethics, value alignment and AI decision theory.
- Philosophy of AI. Intelligence, consciousness, moral status and rights.
AI and Ethics - Role
Course Leader and Sessional Lecturer.
"Overall, to give students the tools needed to reason and make decisions about the ethical, social, and legal aspects of Artificial Intelligence.
More specifically, in three parts on (i) ethics in AI, (ii) algorithmic fairness in ML, and (iii) law and AI, as follows. (i) To present the basic ethical frameworks used in current approaches to ethics in AI, the methods used in designing artificial agents which conform to instances of such frameworks, and to equip the students with the skills of analysis needed to reason about ethical dilemmas in AI. (ii) To present ways of measuring and preventing biased decision making by ML models, and the accuracy/fairness trade-off; to give students the practical tools to define and measure the fairness of ML algorithms. (iii) To present the EU General Data Protection Regulation (GDPR) and its impact on AI that involves personal data, as a key illustrative example of an important law affecting AI/ML, given the risk of large potential fines/compensation claims under the GDPR in practice; other selected laws (e.g., on anti-discrimination) will also be highlighted."
"Upon completion of this module students will be able to:
- Evaluate the ethical and social implications of developments in machine learning and artificial intelligence and critique the technology of autonomous systems.
- Incorporate ethical principles of the key ethical frameworks into the design of artificial agents, according to standard methodologies.
- Analyse the social, ethical, and legal (particularly data protection) barriers to the take-up of AI/ML technologies, including under the GDPR.
- Assess the issues relevant to GDPR-compliant ML technology design and the consequences of non-compliance with legislation such as the GDPR.
- Detect algorithmic bias in machine learning decisions and measure it based on several common metrics.
- Reason about and apply the accuracy-fairness trade-off of machine learning models.
- Evaluate appropriate algorithmic fairness measures to address the bias depending on the task, choose among pre-, in-, or post-processing methods, and perform empirical analysis using appropriate libraries. "
"Ethics and AI (Spring Term)
- Motivating examples: self-driving cars, drones, data storage and usage, bias in ML algorithms.
- Moral dilemmas (inc. the Trolley problem, Plato’s knife, the Samaritan Machine)
- Background and history.
- Ethical paradigms of relevance to AI:
- virtue ethics,
- consequentialism (inc. utilitarianism),
- Practical reasoning and “doing the right thing”; engineering vs ethics.
- Artificial agents and responsibility.
- Types of artificial moral agents (amoral, implicit, explicit).
- Explicit moral agents, rule-based approaches to ethics in AI, logic-based approaches.
- Approaches to building moral agents; top-down vs bottom-up; explainability.
- Building ethical paradigms into AI (selection from Anderson & Anderson, Perreira, Asimov’s rules in football-playing robot, “Moral dilemmas for self-driving cars” study-MIT Media Lab)."